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A developer demonstrates running Gemma 4 26B MoE model locally on an 8GB RTX 4060 with Hermes agent to fully automate backtesting of trading strategies, highlighting the growing capability of local LLMs as autonomous agents.
A user asks why Gemma 4 26b receives less attention compared to Qwen models, sharing their experience using these models for a personal assistant project on a 3090.
An analysis exploring why Gemma 4, despite advantages like QAT and vision support, lacks community finetunes compared to Mistral, and whether community inertia will eventually shift.
Qt Creator 20 now supports local AI coding assistants via the Agent Client Protocol, enabling integration with open-weight models like GPT-OSS and Gemma 4 running on consumer hardware.
The Gemma 4 QAT 31B model demonstrates improved behavior with KV cache quantization, suggesting enhanced inference efficiency.
Discusses running a Q6 quantized version of the Gemma 4 31B model on a dual 9060 XT GPU configuration, likely for local inference.
A new fine-tuned version of Gemma 4 12B, trained on Fable 5's reasoning, achieves a significant jump in agentic coding benchmarks (from 15% to 55%) and can run locally on an 8GB VRAM GPU using a custom fork of llama.cpp.
User reports that Gemma 4 26b outperforms Qwen 3.5/3.6 for language learning and scientific queries, despite being behind in coding tasks, and invites discussion on other non-coding use cases for small MoE models.
A comprehensive 15-part series covering LLM internals from tokenization to serving, grounded in Gemma 4 12B's actual config.
A fine-tuned version of Gemma-4-12B, optimized for local coding and agentic tasks, achieving ~3.5x improvement over the base model on the tau2-bench telecom benchmark.
Gemma 4 12B QAT (dense) achieves over 1000 tokens per second prefill on an 8GB RTX 4060 with 120k context using TurboQuant, enabling full GPU layer offloading. This represents a 42% increase in prefill speed over previous methods.
Mia-AiLab released Gemmable 4 12B, a fine-tuned version of Google's Gemma 4 12B model using Fable-5 style reasoning and assistant traces, available in GGUF and MLX formats for local inference.
Researchers trained a vision-language model without a vision encoder for only $100, inspired by Gemma 4 12B, achieving a 30% reduction in end-to-end latency on an M3 Pro MacBook.
@onusoz demonstrates running 16 parallel instances of NVIDIA's quantized Gemma-4-26B-A4B-NVFP4 model on a single DGX Spark with 128GB unified memory, achieving 300 tok/s aggregate, showcasing high concurrency without flashinfer.
ScreenMind is an open-source tool that uses Gemma 4 to analyze screenshots locally, building a searchable and conversational AI memory bank as a privacy alternative to Microsoft Recall.
Google DeepMind released the Gemma 4 series of open-weight models, covering four sizes from 2B to 31B, supporting 128K–256K context, reasoning, and function calling, under Apache 2.0 license, and equipped with ADK framework for autonomous agent capabilities.
Gemma 4 is demonstrated running in-browser via WebGPU at 255 tokens per second, using kernels generated by Fable 5, showcasing efficient on-device inference.
Phil Schmid highlights that Google's Gemma 4 models enable local agentic coding with about 75% the accuracy/speed of frontier models, referencing a write-up by Vicki Boykis.
User runs Gemma 4 31B dense model on 8GB VRAM gaming laptop at ~3 tokens/sec using llama.cpp with MTP speculative decoding, demonstrating feasibility of running a 31B dense model on consumer hardware and proposing agentic workflows where a fast MoE model routes to this slower dense model for hard tasks.
Gemma 4 E2B achieves 1.3x faster prefill and 2.8x better performance-per-watt on Intel AI PCs using OpenVINO with LiteRT NPU support, enabling efficient background LLM tasks.